CVE-2023-1065

Executive Summary

CVE-2023-1065 is a data-integrity vulnerability in the Snyk Kubernetes Monitor that allows an unauthenticated attacker to inject irrelevant or false data into a target Snyk Organization. Because the monitor trusts input from the Kubernetes environment it is deployed in, a compromised or malicious local cluster component can publish spoofed scan results to the Snyk platform, which in turn obfuscates legitimate security findings — reducing the operator's ability to distinguish real vulnerabilities from injected noise. The successful exploit does not require authentication to Snyk, but it does require knowledge of the target's Integration ID (an unpredictable UUID). The vulnerability carries a CVSS score of 6.5 (High), has no confirmed public PoC, and has no known in-the-wild exploitation per CISA KEV status.

Technical Analysis

The NVD description for CVE-2023-1065 indicates that the defect resides in the Snyk Kubernetes Monitor — a component that forwards container and cluster security data from a Kubernetes environment to the Snyk platform. The monitor is designed to query the local Kubernetes API server, aggregate workload and image metadata, and POST this data to the Snyk backend for analysis.

Vulnerability mechanism: The described flaw is one of insufficient input validation / trust boundary violation. The Snyk Kubernetes Monitor accepts data from the Kubernetes environment (e.g., workload manifests, image tags, namespaces) without correctly verifying the source or content of that data. An attacker who can supply arbitrary data into the monitored Kubernetes environment — for example, by creating a malicious Deployment with crafted annotations or labels, or by injecting a pod spec with fake image metadata — can cause the monitor to forward that bogus data to the Snyk Organization.

The key technical conditions required for exploitation:

1. The attacker does not need authentication credentials to the Snyk platform itself.
2. The attacker must know the target's Integration ID (a UUID). The description notes this may equal the Organization ID, but in either case it is unpredictable.
3. The attacker needs a foothold or control over at least one resource inside (or readable by) the monitored Kubernetes cluster, sufficient to inject data that the monitor will pick up.

Root cause class: This is a classic untrusted input poisoning flaw — the monitor fails to cryptographically sign or verify data provenance between the cluster and the Snyk backend. Because the integration is read-only from the attacker's perspective (no data is exfiltrated), the impact is data integrity degradation, not confidentiality loss.

CWE classification: While no CWE is officially assigned, the vuln is best described as CWE-707 (Improper Neutralization of Special Elements) or CWE-345 (Insufficient Verification of Data Authenticity). Since the NVD lists "N/A", we note this as best-fit.

Attack surface: Any Kubernetes cluster running the Snyk Kubernetes Monitor. The threat model includes:

• Malicious insiders with create permissions on namespaces where the monitor scans.
• Compromised CI/CD pipelines that deploy to monitored namespaces.
• Supply-chain attacks that inject malicious Helm charts/operators into a cluster.

Notably, the vulnerability does not expose Snyk credentials, does not leak user data, and does not allow code execution on Snyk infrastructure. It is a high-availability / integrity nuisance that can blind defenders.

Exploitation Scenarios

No public PoC or exploit code is available. The following attack chain is inferred from the vulnerability class.

Step 1 — Reconnaissance / target identification: To exploit this vulnerability, the attacker first identifies a Kubernetes cluster running the Snyk Kubernetes Monitor. This can be done by:

• Observing network traffic for outbound connections to api.snyk.io or kubernetes-monitor.snyk.io (or associated endpoints).
• Reviewing public container registries for images that include Snyk monitor binaries/labels (e.g., snyk/kubernetes-monitor).
• Leveraging OSINT from compromised CI logs or exposed Grafana/Dashboard instances that show Snyk integration names.

Next, the attacker must determine the target's Integration ID. This UUID may be obtainable through:

• A previous misconfigured webhook or log that exposed it.
• An external container image that embeds Snyk configuration files.
• Social engineering or a prior low-severity compromise of a cluster component.

Step 2 — Triggering the flaw class (insufficient data provenance): The attacker crafts a Kubernetes resource that the monitor will ingest — for example, a Deployment annotated with fake vulnerability metadata, or a Pod whose image tag references a non-existent or malicious image name that the monitor will scan. Because the monitor does not verify the authenticity of the source of the data against a trusted list, it processes these malicious objects as if they were legitimate workloads.

Step 3 — Payload / execution method (vulnerability-class level): The injection occurs at the data plane:

• The attacker creates a workload whose metadata includes attributes that the monitor interprets as security findings (e.g., an annotation claiming a critical vulnerability in an installed image).
• Alternatively, the attacker can spam the cluster API with hundreds of fake namespaces or workloads that carry a high volume of low-severity findings.

The monitor then collects these fabricated findings and POSTs them to the Snyk platform under the target Organization's Integration ID.

Step 4 — Post-exploitation impact:

• The Snyk Organization's dashboard becomes populated with hundreds or thousands of false vulnerability findings.
• Legitimate, critical findings are buried in noise, causing security teams to:
  • Miss real vulnerabilities (increased mean-time-to-remediate).
  • Disable alerts out of alert fatigue (permanent blind spots).
  • Waste time triaging false positives (resource drain).
• Because the attacker only needs periodic access to inject data, the impact persists as long as the bogus workloads exist or the cluster remains compromised.

Impact Assessment

Business impact: The Snyk Kubernetes Monitor is designed to provide continuous security visibility. This vulnerability directly compromises that visibility. For enterprises relying on Snyk for compliance scanning, vulnerability management, and container image gating, the consequence is a degraded security posture — one that cannot distinguish real risk from injected noise.

Affected systems: Any organization running Snyk Kubernetes Monitor with a publicly reachable or internal Kubernetes cluster that is not fully locked down for object creation.

CVSS considerations (score 6.5, High):

• AV:N (Network): The attack is conducted over the network (Kubernetes API / Snyk API).
• AC:L (Low): No special conditions other than knowing a UUID.
• PR:N (None): No Snyk authentication required.
• UI:N (None): No user interaction.
• S:U (Unchanged): Impact is confined to Snyk data integrity, not the underlying cluster.
• C:N (None): No confidentiality loss.
• I:H (High): Complete degradation of data integrity in the Snyk Organization.
• A:N (None): No availability loss.

Blast radius: The primary victim is the Snyk Organization itself. Secondary impact hits downstream consumers of Snyk data (CI/CD gates that block or allow deployments based on Snyk findings, compliance reports, security ticketing integrations). If a CI/CD pipeline automatically blocks deployments when "critical" vulnerabilities are found, an attacker could flip this behavior — either by injecting fake criticals (to halt deployments — DoS) or by flooding with low-severity noise (to prevent real criticals from triggering blocks).

Threat intelligence relevance: EPSS score of 0.00624 (46.8th percentile) indicates low current exploitation likelihood, but the attack vector (unauthenticated, no credentials) makes it attractive for insider-threat and supply-chain attacks. Per CISA KEV, there is no evidence of exploitation in the wild.

Detection Guidance

Sigma Rule

title: Snyk Kubernetes Monitor - Abnormally High Volume of Findings POSTed
id: 8e2a7c94-9f21-4c71-bc1e-ef8333a91a3d
status: experimental
description: Detects a sudden spike in Snyk API POST requests from a single cluster
author: Valters IT CSIRT
logsource:
  category: web
  product: snyk
detection:
  selection_source:
    cs-method: POST
  selection_paths:
    cs-uri-query|contains:
      - 'org'
      - 'integrations'
  selection_volume:
    cs-user-agent|contains: 'snyk'
  condition: selection_source and selection_paths and selection_volume
  timeframe: 1h
  correlation:
    - field: src_ip
      threshold: 100
level: medium
falsepositives:
  - Legitimate large cluster onboarding
  - Bulk image rescan after deployment
tags:
  - attack.t1190
  - attack.t1562
  - cve.2023-1065

YARA Rule

rule Snyk_Kubernetes_Monitor_DataPoison
{
    meta:
        author = "Valters IT CSIRT"
        description = "Detects malicious payloads designed to inject fake findings into Snyk"
        date = "2025-04-14"
        cve = "CVE-2023-1065"
    strings:
        $snyk_endpoint = "snyk.io" ascii nocase
        $integration_id = "integrationId" ascii nocase
        $vuln_field = "vulnerabilities" ascii nocase
        $monitor_name = "kubernetes-monitor" ascii nocase
    condition:
        (2 of ($snyk_endpoint, $integration_id, $monitor_name)) and
        $vuln_field
}

Suricata IDS Rule

alert http any any -> $SNYK_SERVERS any (msg:"CVE-2023-1065 Snyk Kubernetes Monitor - unauth data injection";
flow:to_server,established;
content:"POST"; http_method;
content:"snyk.io"; http_host;
content:"org"; http_uri;
content:"integr"; http_uri;
threshold:type limit, track by_src, count 50, seconds 300;
reference:cve,2023-1065;
classtype:attempted-admin;
sid:20231065; rev:1;)

Elastic Detection

// Elastic Security EQL query for Snyk API anomalies
sequence by host.ip with maxspan=5m
  [ network where dns.question.name : "*snyk.io*" and process.name : "snyk-monitor*" ]
  [ http where url.path : "/api/*/org/*" and request.method == "POST" and event.action == "request" ]
  | stats spikes = count() by url.path, host.ip
  | where spikes > 100

Splunk SPL Query

index=proxy sourcetype=snk:api
| where method="POST" AND uri_path IN ("/api/*/org/*", "/rest/orgs/*")
| stats count as request_count by src_ip, uri_path
| where request_count > 100
| eval severity = if(request_count > 500, "high", "medium")
| table _time, src_ip, uri_path, request_count, severity

Wazuh OSSEC Rule

<group name="snyk,">
  <rule id="352730" level="10">
    <decoded_as>json</decoded_as>
    <field name="host">snyk</field>
    <field name="type">api</field>
    <description>Snyk Kubernetes Monitor: high volume of data received - potential poison</description>
    <group>cve-2023-1065,</group>
    <options>no_full_log</options>
  </rule>
</group>

Mitigation & Patch Guidance

Patch confidence: 3/4 (LIKELY). Two independent sources (CNA and OSV) confirm a fix exists, but the vendor has not published a specific version, commit hash, or advisory URL. The OSV record shows the root package set to "0 → unfixed", implying that the patched version may be a rolling release without a formal CVE-fixed tag.

1) Immediate mitigations (until patched version is confirmed):

• Harden the Kubernetes RBAC: Restrict create and update permissions on Deployments, DaemonSets, and ConfigMaps to only trusted service accounts. Use deny policies in OPA/Gatekeeper to block suspicious annotations or labels on workloads.
• Limit cluster exposure: Ensure the Kubernetes API server is not directly accessible from the internet. Enforce mutual TLS and mTLS for cluster-to-Snyk traffic.
• Monitor outbound traffic: Block all outbound connections to Snyk endpoints except from the IP of the Kubernetes Monitor pod/node.
• Rotate Integration IDs: Regenerating the Integration ID may break attacker knowledge of the UUID (defense-in-depth, not a full fix).

2) Patch / upgrade steps:

• Check the Snyk Kubernetes Monitor deployment channel for updates. If running a Helm chart, run helm upgrade to the latest available chart. If using a container image, update to the latest snyk/kubernetes-monitor tag.
• After upgrading, confirm the monitor version by checking the pod logs or kubectl describe pod — ensure the documented fix for CVE-2023-1065 is present (look for release notes that mention "input validation" or "data integrity").
• If uncertain, contact Snyk support for a specific patched build.

3) Long-term hardening:

• Implement data validation on the Snyk side — organizations should use Snyk reporting filters and custom severity rules to ignore findings from namespaces that are not production-critical.
• Set up baseline alerting in Snyk: if the number of new findings in a 24-hour window exceeds a known threshold, page the on-call team — this catches noise-injection attacks.
• Adopt attestation for image data (e.g., in-toto / cosign) so that the monitor can verify image provenance before reporting vulnerabilities.
• Ensure the integration uses the minimum-privilege Snyk service account (read-only) and that no write tokens are stored on the cluster.

References

1. NVD — CVE-2023-1065: https://nvd.nist.gov/vuln/detail/CVE-2023-1065
2. OSV.dev — CVE-2023-1065: https://osv.dev/vulnerability/CVE-2023-1065 (Package: /, unfixed, patch confirmed by OSV)
3. Snyk Product Security Advisories (vendor page — where patch notes are expected to be published): https://snyk.io/product-security/ (Note: No specific advisory URL published for this CVE as of writing)
4. CISA KEV Catalog: https://www.cisa.gov/known-exploited-vulnerabilities-catalog — CVE-2023-1065 is not listed (no known exploitation).
5. NVD CVSS Calculator (for vector reconstruction): https://nvd.nist.gov/vuln-metrics/cvss/v3-calculator

Threat actor / in-the-wild note: As of this report, there is no evidence of public exploit code, no Nuclei template, and no CISA KEV inclusion. The attack vector is considered a noise-poisoning / integrity-attrition technique, most likely to be used by insider threats, disgruntled contractors with cluster access, or as a supplementary action during a supply-chain compromise. The low EPSS (0.00624) reflects both the limited publicity and the requirement to know a target UUID; however, the lack of authentication makes this an attractive free disruption vector for any attacker already inside a cluster network.